Inpainting repairs defective, unwanted or missing image areas by replacing them with a suitable, adapted piece found elsewhere in the image, preferably without visible artifacts.
Many papers and patents have been published on inpainting. Inpainting typically covers the problem of replacing annoying, unwanted or missing image data with new pixels. Some suggestions have been made to repair the image area with repeated use of convolution kernels, while some suggest or implement the use of replacement pixels or replacement structure.
Routines work well only when the user provides a suggestion to the algorithm as to which image area might make a good candidate for replacing the area to be inpainted. Routines that try to find replacement data automatically do not work well once the replacement area is large (larger than 1% of the image). What is needed is a system that does a better job in finding replacement areas, and that can assemble replacement image data comprised of multiple smaller areas if the area to be inpainted is very large.